enum [Boolean] — Validates that a string value matches one
of the allowable enum values.

computeChecksums(Boolean)
—

whether to compute checksums
for payload bodies when the service accepts it (currently supported
in S3 only)

convertResponseTypes(Boolean)
—

whether types are converted
when parsing response data. Currently only supported for JSON based
services. Turning this off may improve performance on large response
payloads. Defaults to true.

correctClockSkew(Boolean)
—

whether to apply a clock skew
correction and retry requests that fail because of an skewed client
clock. Defaults to false.

s3ForcePathStyle(Boolean)
—

whether to force path
style URLs for S3 objects.

s3BucketEndpoint(Boolean)
—

whether the provided endpoint
addresses an individual bucket (false if it addresses the root API
endpoint). Note that setting this configuration option requires an
endpoint to be provided explicitly to the service constructor.

s3DisableBodySigning(Boolean)
—

whether S3 body signing
should be disabled when using signature version v4. Body signing
can only be disabled when using https. Defaults to true.

retryDelayOptions(map)
—

A set of options to configure
the retry delay on retryable errors. Currently supported options are:

base [Integer] — The base number of milliseconds to use in the
exponential backoff for operation retries. Defaults to 100 ms for all
services except DynamoDB, where it defaults to 50ms.

customBackoff [function] — A custom function that accepts a retry count
and returns the amount of time to delay in milliseconds. The base option will be
ignored if this option is supplied.

httpOptions(map)
—

A set of options to pass to the low-level
HTTP request. Currently supported options are:

proxy [String] — the URL to proxy requests through

agent [http.Agent, https.Agent] — the Agent object to perform
HTTP requests with. Used for connection pooling. Defaults to the global
agent (http.globalAgent) for non-SSL connections. Note that for
SSL connections, a special Agent object is used in order to enable
peer certificate verification. This feature is only available in the
Node.js environment.

connectTimeout [Integer] — Sets the socket to timeout after
failing to establish a connection with the server after
connectTimeout milliseconds. This timeout has no effect once a socket
connection has been established.

timeout [Integer] — Sets the socket to timeout after timeout
milliseconds of inactivity on the socket. Defaults to two minutes
(120000).

xhrAsync [Boolean] — Whether the SDK will send asynchronous
HTTP requests. Used in the browser environment only. Set to false to
send requests synchronously. Defaults to true (async on).

xhrWithCredentials [Boolean] — Sets the "withCredentials"
property of an XMLHttpRequest object. Used in the browser environment
only. Defaults to false.

apiVersion(String, Date)
—

a String in YYYY-MM-DD format
(or a date) that represents the latest possible API version that can be
used in all services (unless overridden by apiVersions). Specify
'latest' to use the latest possible version.

apiVersions(map<String, String|Date>)
—

a map of service
identifiers (the lowercase service class name) with the API version to
use when instantiating a service. Specify 'latest' for each individual
that can use the latest available version.

logger(#write, #log)
—

an object that responds to .write()
(like a stream) or .log() (like the console object) in order to log
information about requests

systemClockOffset(Number)
—

an offset value in milliseconds
to apply to all signing times. Use this to compensate for clock skew
when your system may be out of sync with the service time. Note that
this configuration option can only be applied to the global AWS.config
object and cannot be overridden in service-specific configuration.
Defaults to 0 milliseconds.

Method Details

Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, AddTags updates the tag's value.

Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources.

CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.

You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.

A user-supplied name or description of the BatchPrediction. BatchPredictionName can only use the UTF-8 character set.

MLModelId — (String)

The ID of the MLModel that will generate predictions for the group of observations.

BatchPredictionDataSourceId — (String)

The ID of the DataSource that points to the group of observations to predict.

OutputUri — (String)

The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory to store the batch prediction results. The following substrings are not allowed in the s3 key portion of the outputURI field: ':', '//', '/./', '/../'.

Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used only to perform >CreateMLModel>, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.

ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see Role templates for data pipelines.

ServiceRole - A role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

SecurityInfo - The security information to use to access an RDS DB instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [SubnetId, SecurityGroupIds] pair for a VPC-based RDS DB instance.

SelectSqlQuery - A query that is used to retrieve the observation data for the Datasource.

S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.

DataSchemaUri - The Amazon S3 location of the DataSchema.

DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.

DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the Datasource.

Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"

DatabaseInformation — required — (map)

Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

InstanceIdentifier — required — (String)

The ID of an RDS DB instance.

DatabaseName — required — (String)

The name of a database hosted on an RDS DB instance.

SelectSqlQuery — required — (String)

The query that is used to retrieve the observation data for the DataSource.

DatabaseCredentials — required — (map)

The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.

Username — required — (String)

The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an RDSSelectSqlQuery query.

Password — required — (String)

The password to be used by Amazon ML to connect to a database on an RDS DB instance. The password should have sufficient permissions to execute the RDSSelectQuery query.

S3StagingLocation — required — (String)

The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.

DataRearrangement — (String)

A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

There are multiple parameters that control what data is used to create a datasource:

percentBegin

Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

percentEnd

Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

complement

The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

To change how Amazon ML splits the data for a datasource, use the strategy parameter.

The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.

ServiceRole — required — (String)

The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

SubnetId — required — (String)

The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.

SecurityGroupIds — required — (Array<String>)

The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.

RoleARN — (String)

The role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user's account and copy data using the SelectSqlQuery query from Amazon RDS to Amazon S3.

ComputeStatistics — (Boolean)

The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training.

Callback (callback):

function(err, data) { ... }

Called when a response from the service is returned. If a
callback is not supplied, you must call AWS.Request.send()
on the returned request object to initiate the request.

Creates a DataSource from a database hosted on an Amazon Redshift cluster. A DataSource references data that can be used to perform either CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING states can be used to perform only CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to transfer the result set of the SelectSqlQuery query to S3StagingLocation.

After the DataSource has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also requires a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.

<?oxy_insert_start author="laurama" timestamp="20160406T153842-0700">

You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing datasource and copy the values to a CreateDataSource call. Change the settings that you want to change and make sure that all required fields have the appropriate values.

DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.

SelectSqlQuery - The query that is used to retrieve the observation data for the Datasource.

S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the SelectSqlQuery query is stored in this location.

DataSchemaUri - The Amazon S3 location of the DataSchema.

DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.

DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the DataSource.

Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"

DatabaseInformation — required — (map)

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

DatabaseName — required — (String)

The name of a database hosted on an Amazon Redshift cluster.

ClusterIdentifier — required — (String)

The ID of an Amazon Redshift cluster.

SelectSqlQuery — required — (String)

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

DatabaseCredentials — required — (map)

Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.

Username — required — (String)

A username to be used by Amazon Machine Learning (Amazon ML)to connect to a database on an Amazon Redshift cluster. The username should have sufficient permissions to execute the RedshiftSelectSqlQuery query. The username should be valid for an Amazon Redshift USER.

Password — required — (String)

A password to be used by Amazon ML to connect to a database on an Amazon Redshift cluster. The password should have sufficient permissions to execute a RedshiftSelectSqlQuery query. The password should be valid for an Amazon Redshift USER.

S3StagingLocation — required — (String)

Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

DataRearrangement — (String)

A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

There are multiple parameters that control what data is used to create a datasource:

percentBegin

Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

percentEnd

Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

complement

The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

To change how Amazon ML splits the data for a datasource, use the strategy parameter.

The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training.

Callback (callback):

function(err, data) { ... }

Called when a response from the service is returned. If a
callback is not supplied, you must call AWS.Request.send()
on the returned request object to initiate the request.

Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used to perform only CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.

If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observation data used in a DataSource should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource.

After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.

DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.

DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the Datasource.

Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"

DataLocationS3 — required — (String)

The location of the data file(s) used by a DataSource. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.

DataRearrangement — (String)

A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

There are multiple parameters that control what data is used to create a datasource:

percentBegin

Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

percentEnd

Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

complement

The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

To change how Amazon ML splits the data for a datasource, use the strategy parameter.

The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

Describes the schema location in Amazon S3. You must provide either the DataSchema or the DataSchemaLocationS3.

ComputeStatistics — (Boolean)

The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training.

Callback (callback):

function(err, data) { ... }

Called when a response from the service is returned. If a
callback is not supplied, you must call AWS.Request.send()
on the returned request object to initiate the request.

Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.

CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.

You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.

Creates a new MLModel using the DataSource and the recipe as information sources.

An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.

CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED.

You can use the GetMLModel operation to check the progress of the MLModel during the creation operation.

CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We <?oxy_insert_start author="laurama" timestamp="20160329T131121-0700">strongly recommend that you shuffle your data.<?oxy_insert_end>

sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.

The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.

sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.

The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

TrainingDataSourceId — (String)

The DataSource that points to the training data.

Recipe — (String)

The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

RecipeUri — (String)

The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

Callback (callback):

function(err, data) { ... }

Called when a response from the service is returned. If a
callback is not supplied, you must call AWS.Request.send()
on the returned request object to initiate the request.

Name - Sets the search criteria to the contents of the BatchPredictionName.

IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation.

MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction.

DataSourceId - Sets the search criteria to the DataSource used in the BatchPrediction.

DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.

Possible values include:

"CreatedAt"

"LastUpdatedAt"

"Status"

"Name"

"IAMUser"

"MLModelId"

"DataSourceId"

"DataURI"

EQ — (String)

The equal to operator. The BatchPrediction results will have FilterVariable values that exactly match the value specified with EQ.

GT — (String)

The greater than operator. The BatchPrediction results will have FilterVariable values that are greater than the value specified with GT.

LT — (String)

The less than operator. The BatchPrediction results will have FilterVariable values that are less than the value specified with LT.

GE — (String)

The greater than or equal to operator. The BatchPrediction results will have FilterVariable values that are greater than or equal to the value specified with GE.

LE — (String)

The less than or equal to operator. The BatchPrediction results will have FilterVariable values that are less than or equal to the value specified with LE.

NE — (String)

The not equal to operator. The BatchPrediction results will have FilterVariable values not equal to the value specified with NE.

Prefix — (String)

A string that is found at the beginning of a variable, such as Name or Id.

For example, a Batch Prediction operation could have the Name2014-09-09-HolidayGiftMailer. To search for this BatchPrediction, select Name for the FilterVariable and any of the following strings for the Prefix:

2014-09

2014-09-09

2014-09-09-Holiday

SortOrder — (String)

A two-value parameter that determines the sequence of the resulting list of MLModels.

asc - Arranges the list in ascending order (A-Z, 0-9).

dsc - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by FilterVariable.

Possible values include:

"asc"

"dsc"

NextToken — (String)

An ID of the page in the paginated results.

Limit — (Integer)

The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.

Callback (callback):

function(err, data) { ... }

Called when a response from the service is returned. If a
callback is not supplied, you must call AWS.Request.send()
on the returned request object to initiate the request.

FAILED - The request to create a DataSource did not run to completion. It is not usable.

COMPLETED - The creation process completed successfully.

DELETED - The DataSource is marked as deleted. It is not usable.

Possible values include:

"PENDING"

"INPROGRESS"

"FAILED"

"COMPLETED"

"DELETED"

Message — (String)

A description of the most recent details about creating the DataSource.

RedshiftMetadata — (map)

Describes the DataSource details specific to Amazon Redshift.

RedshiftDatabase — (map)

Describes the database details required to connect to an Amazon Redshift database.

DatabaseName — required — (String)

The name of a database hosted on an Amazon Redshift cluster.

ClusterIdentifier — required — (String)

The ID of an Amazon Redshift cluster.

DatabaseUserName — (String)

A username to be used by Amazon Machine Learning (Amazon ML)to connect to a database on an Amazon Redshift cluster. The username should have sufficient permissions to execute the RedshiftSelectSqlQuery query. The username should be valid for an Amazon Redshift USER.

The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an RDSSelectSqlQuery query.

SelectSqlQuery — (String)

The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.

ResourceRole — (String)

The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

ServiceRole — (String)

The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

DataPipelineId — (String)

The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console.

RoleARN — (String)

The Amazon Resource Name (ARN) of an AWS IAM Role, such as the following: arn:aws:iam::account:role/rolename.

ComputeStatistics — (Boolean)

The parameter is true if statistics need to be generated from the observation data.

ComputeTime — (Integer)

Long integer type that is a 64-bit signed number.

FinishedAt — (Date)

A timestamp represented in epoch time.

StartedAt — (Date)

A timestamp represented in epoch time.

NextToken — (String)

An ID of the next page in the paginated results that indicates at least one more page follows.

FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

COMPLETED - The creation process completed successfully.

DELETED - The MLModel is marked as deleted. It isn't usable.

Possible values include:

"PENDING"

"INPROGRESS"

"FAILED"

"COMPLETED"

"DELETED"

SizeInBytes — (Integer)

Long integer type that is a 64-bit signed number.

EndpointInfo — (map)

The current endpoint of the MLModel.

PeakRequestsPerSecond — (Integer)

The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.

CreatedAt — (Date)

The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

EndpointUrl — (String)

The URI that specifies where to send real-time prediction requests for the MLModel.

Note:Note The application must wait until the real-time endpoint is ready before using this URI.

EndpointStatus — (String)

The current status of the real-time endpoint for the MLModel. This element can have one of the following values:

NONE - Endpoint does not exist or was previously deleted.

READY - Endpoint is ready to be used for real-time predictions.

UPDATING - Updating/creating the endpoint.

Possible values include:

"NONE"

"READY"

"UPDATING"

"FAILED"

TrainingParameters — (map<String>)

A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.

sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.

The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.

sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.

The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

InputDataLocationS3 — (String)

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Algorithm — (String)

The algorithm used to train the MLModel. The following algorithm is supported:

SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

Possible values include:

"sgd"

MLModelType — (String)

Identifies the MLModel category. The following are the available types:

REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

FAILED - The request to perform a batch prediction did not run to completion. It is not usable.

COMPLETED - The batch prediction process completed successfully.

DELETED - The BatchPrediction is marked as deleted. It is not usable.

Possible values include:

"PENDING"

"INPROGRESS"

"FAILED"

"COMPLETED"

"DELETED"

OutputUri — (String)

The location of an Amazon S3 bucket or directory to receive the operation results.

LogUri — (String)

A link to the file that contains logs of the CreateBatchPrediction operation.

Message — (String)

A description of the most recent details about processing the batch prediction request.

ComputeTime — (Integer)

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the BatchPrediction, normalized and scaled on computation resources. ComputeTime is only available if the BatchPrediction is in the COMPLETED state.

FinishedAt — (Date)

The epoch time when Amazon Machine Learning marked the BatchPrediction as COMPLETED or FAILED. FinishedAt is only available when the BatchPrediction is in the COMPLETED or FAILED state.

StartedAt — (Date)

The epoch time when Amazon Machine Learning marked the BatchPrediction as INPROGRESS. StartedAt isn't available if the BatchPrediction is in the PENDING state.

TotalRecordCount — (Integer)

The number of total records that Amazon Machine Learning saw while processing the BatchPrediction.

InvalidRecordCount — (Integer)

The number of invalid records that Amazon Machine Learning saw while processing the BatchPrediction.

the response object containing error, data properties, and the original request object.

Parameters:

err(Error)
—

the error object returned from the request.
Set to null if the request is successful.

data(Object)
—

the de-serialized data returned from
the request. Set to null if a request error occurs.
The data object has the following properties:

DataSourceId — (String)

The ID assigned to the DataSource at creation. This value should be identical to the value of the DataSourceId in the request.

DataLocationS3 — (String)

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

DataRearrangement — (String)

A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created.

CreatedByIamUser — (String)

The AWS user account from which the DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

CreatedAt — (Date)

The time that the DataSource was created. The time is expressed in epoch time.

LastUpdatedAt — (Date)

The time of the most recent edit to the DataSource. The time is expressed in epoch time.

DataSizeInBytes — (Integer)

The total size of observations in the data files.

NumberOfFiles — (Integer)

The number of data files referenced by the DataSource.

Name — (String)

A user-supplied name or description of the DataSource.

Status — (String)

The current status of the DataSource. This element can have one of the following values:

PENDING - Amazon ML submitted a request to create a DataSource.

INPROGRESS - The creation process is underway.

FAILED - The request to create a DataSource did not run to completion. It is not usable.

COMPLETED - The creation process completed successfully.

DELETED - The DataSource is marked as deleted. It is not usable.

Possible values include:

"PENDING"

"INPROGRESS"

"FAILED"

"COMPLETED"

"DELETED"

LogUri — (String)

A link to the file containing logs of CreateDataSourceFrom* operations.

Message — (String)

The user-supplied description of the most recent details about creating the DataSource.

RedshiftMetadata — (map)

Describes the DataSource details specific to Amazon Redshift.

RedshiftDatabase — (map)

Describes the database details required to connect to an Amazon Redshift database.

DatabaseName — required — (String)

The name of a database hosted on an Amazon Redshift cluster.

ClusterIdentifier — required — (String)

The ID of an Amazon Redshift cluster.

DatabaseUserName — (String)

A username to be used by Amazon Machine Learning (Amazon ML)to connect to a database on an Amazon Redshift cluster. The username should have sufficient permissions to execute the RedshiftSelectSqlQuery query. The username should be valid for an Amazon Redshift USER.

The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an RDSSelectSqlQuery query.

SelectSqlQuery — (String)

The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.

ResourceRole — (String)

The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

ServiceRole — (String)

The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

DataPipelineId — (String)

The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console.

RoleARN — (String)

The Amazon Resource Name (ARN) of an AWS IAM Role, such as the following: arn:aws:iam::account:role/rolename.

ComputeStatistics — (Boolean)

The parameter is true if statistics need to be generated from the observation data.

ComputeTime — (Integer)

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the DataSource, normalized and scaled on computation resources. ComputeTime is only available if the DataSource is in the COMPLETED state and the ComputeStatistics is set to true.

FinishedAt — (Date)

The epoch time when Amazon Machine Learning marked the DataSource as COMPLETED or FAILED. FinishedAt is only available when the DataSource is in the COMPLETED or FAILED state.

StartedAt — (Date)

The epoch time when Amazon Machine Learning marked the DataSource as INPROGRESS. StartedAt isn't available if the DataSource is in the PENDING state.

A link to the file that contains logs of the CreateEvaluation operation.

Message — (String)

A description of the most recent details about evaluating the MLModel.

ComputeTime — (Integer)

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the Evaluation, normalized and scaled on computation resources. ComputeTime is only available if the Evaluation is in the COMPLETED state.

FinishedAt — (Date)

The epoch time when Amazon Machine Learning marked the Evaluation as COMPLETED or FAILED. FinishedAt is only available when the Evaluation is in the COMPLETED or FAILED state.

StartedAt — (Date)

The epoch time when Amazon Machine Learning marked the Evaluation as INPROGRESS. StartedAt isn't available if the Evaluation is in the PENDING state.

FAILED - The request did not run to completion. The ML model isn't usable.

COMPLETED - The request completed successfully.

DELETED - The MLModel is marked as deleted. It isn't usable.

Possible values include:

"PENDING"

"INPROGRESS"

"FAILED"

"COMPLETED"

"DELETED"

SizeInBytes — (Integer)

Long integer type that is a 64-bit signed number.

EndpointInfo — (map)

The current endpoint of the MLModel

PeakRequestsPerSecond — (Integer)

The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.

CreatedAt — (Date)

The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

EndpointUrl — (String)

The URI that specifies where to send real-time prediction requests for the MLModel.

Note:Note The application must wait until the real-time endpoint is ready before using this URI.

EndpointStatus — (String)

The current status of the real-time endpoint for the MLModel. This element can have one of the following values:

NONE - Endpoint does not exist or was previously deleted.

READY - Endpoint is ready to be used for real-time predictions.

UPDATING - Updating/creating the endpoint.

Possible values include:

"NONE"

"READY"

"UPDATING"

"FAILED"

TrainingParameters — (map<String>)

A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data.

sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.

The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.

sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.

The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

InputDataLocationS3 — (String)

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

MLModelType — (String)

Identifies the MLModel category. The following are the available types:

REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"

BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"

MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"

Possible values include:

"REGRESSION"

"BINARY"

"MULTICLASS"

ScoreThreshold — (Float)

The scoring threshold is used in binary classification MLModel<?oxy_insert_start author="laurama" timestamp="20160329T114851-0700"> <?oxy_insert_end>models. It marks the boundary between a positive prediction and a negative prediction.

Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

ScoreThresholdLastUpdatedAt — (Date)

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

LogUri — (String)

A link to the file that contains logs of the CreateMLModel operation.

Message — (String)

A description of the most recent details about accessing the MLModel.

ComputeTime — (Integer)

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state.

FinishedAt — (Date)

The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.

StartedAt — (Date)

The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

Recipe — (String)

The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

Note:Note This parameter is provided as part of the verbose format.

Schema — (String)

The schema used by all of the data files referenced by the DataSource.

The ScoreThreshold used in binary classification MLModel that marks the boundary between a positive prediction and a negative prediction.

Output values greater than or equal to the ScoreThreshold receive a positive result from the MLModel, such as true. Output values less than the ScoreThreshold receive a negative response from the MLModel, such as false.

Callback (callback):

function(err, data) { ... }

Called when a response from the service is returned. If a
callback is not supplied, you must call AWS.Request.send()
on the returned request object to initiate the request.

Waits for a given MachineLearning resource. The final callback or
'complete' event will be fired only when the resource
is either in its final state or the waiter has timed out and stopped polling
for the final state.

FAILED - The request to create a DataSource did not run to completion. It is not usable.

COMPLETED - The creation process completed successfully.

DELETED - The DataSource is marked as deleted. It is not usable.

Possible values include:

"PENDING"

"INPROGRESS"

"FAILED"

"COMPLETED"

"DELETED"

Message — (String)

A description of the most recent details about creating the DataSource.

RedshiftMetadata — (map)

Describes the DataSource details specific to Amazon Redshift.

RedshiftDatabase — (map)

Describes the database details required to connect to an Amazon Redshift database.

DatabaseName — required — (String)

The name of a database hosted on an Amazon Redshift cluster.

ClusterIdentifier — required — (String)

The ID of an Amazon Redshift cluster.

DatabaseUserName — (String)

A username to be used by Amazon Machine Learning (Amazon ML)to connect to a database on an Amazon Redshift cluster. The username should have sufficient permissions to execute the RedshiftSelectSqlQuery query. The username should be valid for an Amazon Redshift USER.

The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an RDSSelectSqlQuery query.

SelectSqlQuery — (String)

The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.

ResourceRole — (String)

The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

ServiceRole — (String)

The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

DataPipelineId — (String)

The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console.

RoleARN — (String)

The Amazon Resource Name (ARN) of an AWS IAM Role, such as the following: arn:aws:iam::account:role/rolename.

ComputeStatistics — (Boolean)

The parameter is true if statistics need to be generated from the observation data.

ComputeTime — (Integer)

Long integer type that is a 64-bit signed number.

FinishedAt — (Date)

A timestamp represented in epoch time.

StartedAt — (Date)

A timestamp represented in epoch time.

NextToken — (String)

An ID of the next page in the paginated results that indicates at least one more page follows.

FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

COMPLETED - The creation process completed successfully.

DELETED - The MLModel is marked as deleted. It isn't usable.

Possible values include:

"PENDING"

"INPROGRESS"

"FAILED"

"COMPLETED"

"DELETED"

SizeInBytes — (Integer)

Long integer type that is a 64-bit signed number.

EndpointInfo — (map)

The current endpoint of the MLModel.

PeakRequestsPerSecond — (Integer)

The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.

CreatedAt — (Date)

The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

EndpointUrl — (String)

The URI that specifies where to send real-time prediction requests for the MLModel.

Note:Note The application must wait until the real-time endpoint is ready before using this URI.

EndpointStatus — (String)

The current status of the real-time endpoint for the MLModel. This element can have one of the following values:

NONE - Endpoint does not exist or was previously deleted.

READY - Endpoint is ready to be used for real-time predictions.

UPDATING - Updating/creating the endpoint.

Possible values include:

"NONE"

"READY"

"UPDATING"

"FAILED"

TrainingParameters — (map<String>)

A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.

sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.

The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.

sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.

The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

InputDataLocationS3 — (String)

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Algorithm — (String)

The algorithm used to train the MLModel. The following algorithm is supported:

SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

Possible values include:

"sgd"

MLModelType — (String)

Identifies the MLModel category. The following are the available types:

REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

Name - Sets the search criteria to the contents of the BatchPredictionName.

IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation.

MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction.

DataSourceId - Sets the search criteria to the DataSource used in the BatchPrediction.

DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.

Possible values include:

"CreatedAt"

"LastUpdatedAt"

"Status"

"Name"

"IAMUser"

"MLModelId"

"DataSourceId"

"DataURI"

EQ — (String)

The equal to operator. The BatchPrediction results will have FilterVariable values that exactly match the value specified with EQ.

GT — (String)

The greater than operator. The BatchPrediction results will have FilterVariable values that are greater than the value specified with GT.

LT — (String)

The less than operator. The BatchPrediction results will have FilterVariable values that are less than the value specified with LT.

GE — (String)

The greater than or equal to operator. The BatchPrediction results will have FilterVariable values that are greater than or equal to the value specified with GE.

LE — (String)

The less than or equal to operator. The BatchPrediction results will have FilterVariable values that are less than or equal to the value specified with LE.

NE — (String)

The not equal to operator. The BatchPrediction results will have FilterVariable values not equal to the value specified with NE.

Prefix — (String)

A string that is found at the beginning of a variable, such as Name or Id.

For example, a Batch Prediction operation could have the Name2014-09-09-HolidayGiftMailer. To search for this BatchPrediction, select Name for the FilterVariable and any of the following strings for the Prefix:

2014-09

2014-09-09

2014-09-09-Holiday

SortOrder — (String)

A two-value parameter that determines the sequence of the resulting list of MLModels.

asc - Arranges the list in ascending order (A-Z, 0-9).

dsc - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by FilterVariable.

Possible values include:

"asc"

"dsc"

NextToken — (String)

An ID of the page in the paginated results.

Limit — (Integer)

The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.

Callback (callback):

function(err, data) { ... }

Called when a response from the service is returned. If a
callback is not supplied, you must call AWS.Request.send()
on the returned request object to initiate the request.